| import json |
| import os |
| from pathlib import Path |
| import datasets |
|
|
|
|
| _CITATION = """ |
| @misc{gurari2018vizwiz, |
| title={VizWiz Grand Challenge: Answering Visual Questions from Blind People}, |
| author={Danna Gurari and Qing Li and Abigale J. Stangl and Anhong Guo and Chi Lin and Kristen Grauman and Jiebo Luo and Jeffrey P. Bigham}, |
| year={2018}, |
| eprint={1802.08218}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CV} |
| } |
| """ |
|
|
| _HOMEPAGE = "https://vizwiz.org/tasks-and-datasets/vqa/" |
|
|
| _DESCRIPTION = """ |
| The VizWiz-VQA dataset originates from a natural visual question answering setting where blind people |
| each took an image and recorded a spoken question about it, together with 10 crowdsourced answers per |
| visual question. The proposed challenge addresses the following two tasks for this dataset: predict the |
| answer to a visual question and (2) predict whether a visual question cannot be answered. |
| """ |
|
|
| _LICENSE = " Creative Commons Attribution 4.0 International License." |
|
|
| _DATA_URL = {"train" : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/train.zip", |
| "test" : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/test.zip", |
| "val" : "https://vizwiz.cs.colorado.edu/VizWiz_final/images/val.zip" } |
|
|
| _ANNOTATION_URL = "https://vizwiz.cs.colorado.edu/VizWiz_final/vqa_data/Annotations.zip" |
|
|
| _FEATURES = datasets.Features( |
| { |
| "id" : datasets.Value("int32"), |
| "image": datasets.Image(), |
| "filename": datasets.Value("string"), |
| "question": datasets.Value("string"), |
| "answers": datasets.Sequence(datasets.Value("string")), |
| "answers_original": [ |
| { |
| "answer": datasets.Value("string"), |
| "answer_confidence": datasets.Value("string"), |
| } |
| ], |
| "answer_type": datasets.Value("string"), |
| "answerable": datasets.Value("int32") |
| } |
| ) |
|
|
|
|
| class VizWiz(datasets.GeneratorBasedBuilder): |
| VERSION = datasets.Version("1.0.0") |
| def _info(self): |
| return datasets.DatasetInfo( |
| description = _DESCRIPTION, |
| features = _FEATURES, |
| homepage = _HOMEPAGE, |
| license = _LICENSE, |
| citation = _CITATION, |
| ) |
| |
| def _split_generators(self, dl_manager): |
| ann_file_train = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "train.json") |
| ann_file_val = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "val.json") |
| ann_file_test = os.path.join(dl_manager.download_and_extract(_ANNOTATION_URL), "test.json") |
| image_folders = {k: Path(v) for k, v in dl_manager.download_and_extract(_DATA_URL).items()} |
|
|
| return [ |
| datasets.SplitGenerator( |
| name=datasets.Split.TRAIN, |
| gen_kwargs={ |
| "annotation_file": ann_file_train, |
| "image_folders": image_folders, |
| "split_key": 'train' |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.VALIDATION, |
| gen_kwargs={ |
| "annotation_file": ann_file_val, |
| "image_folders": image_folders, |
| "split_key": "val" |
| }, |
| ), |
| datasets.SplitGenerator( |
| name=datasets.Split.TEST, |
| gen_kwargs={ |
| "annotation_file": ann_file_test, |
| "image_folders": image_folders, |
| "split_key": "test" |
| }, |
| ), |
| ] |
| |
| def _generate_examples(self, annotation_file,image_folders,split_key): |
| counter = 0 |
| annotations = json.load(open(annotation_file)) |
| for ann in annotations: |
| if split_key in ['train','val']: |
| answers = [answer["answer"] for answer in ann["answers"]] |
| answers_original = ann['answers'] |
| answer_type = ann["answer_type"] |
| answerable = ann["answerable"] |
| |
| else: |
| |
| answers = None |
| answers_original = None |
| answer_type = None |
| answerable = None |
| |
| yield counter, { |
| "id" : counter, |
| "image": str(image_folders[split_key]/split_key/ann['image']), |
| "filename" : ann['image'], |
| "question" : ann["question"], |
| "answers" : answers, |
| "answers_original" : answers_original, |
| "answer_type" : answer_type, |
| "answerable" : answerable |
| } |
| counter += 1 |
|
|
